A goal of many different online enterprises can be to understand the visitors to particular websites and webpages. (See, e.g., Reference 8). Understanding one property of online visitors—the interests of these visitors to a particular website or webpage, (e.g., the audience interests of that website or webpage)—can be especially beneficial to a variety of online players. Knowledge of audience interests can facilitate website operators to optimize their content and navigation, create better content for their audience, improve site merchandizing such as the placement of product links and internal offers, solicit sponsorship, and perform other audience analytics. In addition, understanding audience interests can be a key goal of many players in the online advertising industry, where advertisers can associate brand advertisements with the interests of website visitors. For example, Proctor & Gamble may want to place Olay advertisements on webpages whose audience interests include the category “beauty”.
Previous research has been done related to audience interest model and study. Behavioral targeting (“BT”) procedures (see, e.g., Reference 5) can analyze historical user behavior in an attempt to deliver relevant advertisements to the user. BT aims to increase advertising revenue through maximizing proxy measures such as the click through rate (“CTR”) (e.g., the percentage of browsers who click on an advertisement, out of the total number of browsers who are shown the advertisement) or conversions. (See, e.g., Reference 33). (See, e.g., Reference 23) Other procedures can extract quasi-social networks from users' browsing behavior for the purpose of improving brand advertising targeting. Similar to BT, user interests can be modeled from users' browsing behavior.
Contextual targeting (“CT”) procedures (see, e.g., References 4, 24) aim to place advertisements that match the content of the websites, so as to increase revenue of both publishers and ad-networks, and also to improve user experience. For example, previous methods propose to integrate behavioral targeting into contextual advertising to improve the relevance of advertisements retrieved. (See, e.g., Reference 17) However, (i) CT does not model/profile user interests, but focuses on content of websites; (ii) CT focuses specifically on the interests represented explicitly on the webpages, rather than the more general interests of the audience, and (iii) the goal of CT can be to maximize advertising revenue,
Outside of the realm of web audience analysis, techniques for recommender systems have been proposed. (See, e.g., Reference 1) The majority of the use of recommender systems can be based on two main approaches, content filtering and collaborative filtering. Content filtering (see, e.g., Reference 18) can build profiles for items (e.g., actors, directors, and genres for movies, etc.) and users (e.g., demographic information, and information through explicit user feedback) in order to recommend items similar to those items a given user may have liked in the past. For example, previous methods describe a learning-driven client-side keyword-based personalization approach for search advertising. (See, e.g., Reference 3). They can allow advertisers to customize existing search advertising campaigns based on users' prior behavior, while facilitating users to opt out from server-side storage of their behavioral history. For example, previous work describes predictive bilinear regression models which can be used to combine both profiles of contents (e.g., popularity and freshness) and profiles of users (e.g., demographic information, and summary of online activities) in order to provide personalized recommendations of new items to users.
Collaborative filtering can exploit relationships between users and interdependencies among items. Both neighborhood methods (e.g., computing similarities between users and/or items, (see, e.g., Reference 10) and matrix factorization methods (e.g., extracting latent factors characterizing users and items, (see, e.g., Reference 16)) have been studied extensively. Collaborative filtering based recommendation systems have been proposed which illustrate that the recommender-system-induced graphs generally provide a better match with the real-world consumer-product graphs than purely random graphs. New developments can extend the range of recommender systems to group-level recommenders, for example, to recommend a joint skiing vacation for a group of friends. (See, e.g., Reference 29). They can also propose to improve individual-level rating predictions by relying on aggregate rating data.
Thus, it may be beneficial to provide exemplary systems, methods and computer-accessible mediums that can estimate the distribution of a target website's audience interests based one users' online behavior, and which can overcome at least some of the deficiencies described herein above.